Automated Detection and Classification of Microcalcification Clusters with Enhanced Preprocessing and Fractal Analysis

Abstract

This paper addresses the automated detection of microcalcification clusters from mammogram images by enhanced<br />preprocessing operations on digital mammograms for automated extraction of breast tissue from background, removing<br />artefacts occurring during image registration using X-rays, followed by fractal analysis of suspicious regions.<br />Identification of breast of either left or right and realigning them to a standard position forms a primitive step in<br />preprocessing of mammograms. As the next step in the process, pectoral muscles are separated. Suspicious regions of<br />microcalcifications are identified and are subjected to further analysis of classifying it as benign or malignant. Texture<br />features are representative of its malignancy and fractal analysis was carried out on extracted suspicious regions for<br />its texture features. Principal Component Analysis was carried out to extract optimal features. Ten features were<br />found to be an optimal number of reduced texture features without compromising on classification accuracy. Scaled<br />conjugate Gradient Back propagation network was used for classification using reduced texture features obtained from<br />PCA analysis. By varying hidden layer neurons, accuracy of results achieved by proposed methods is analysed and<br />is calculated to reach maximum accuracy with an optimal level of 15 neurons. Accuracy of 96.3% was achieved with<br />10 fractal features as input to neural network and 15 hidden layer neurons in neural network designed. The design of<br />architecture is finalised with maximised accuracy for labelling microcalcification clusters as benign or malignant.